The rapid development and popularization of smart mobile devices has hastened the emergence and development of indoor (or locally regional) positioning techniques, which mainly adopt integration of various techniques such as wireless communication, base station positioning and inertial navigation positioning to form a set of indoor position positioning systems, so as to realize position monitoring of personnel, objects, and the like in indoor spaces. There is wide demand and application of indoor position techniques in many fields such as commercial applications, public security and military scenarios.
Indoor positioning is usually realized by adopting a triangular positioning method based on (Received Signal Strength Indication (RSSI) and a fingerprint method. Since RSSI is influenced by a multipath effect due to various factors of environments, the error rate of RSSI is great. As a result, the triangular positioning method based on RSSI is gradually being replaced by the fingerprint method. The fingerprint method comprises two steps, wherein the first step is fingerprint database drawing and the second step is real-time positioning. The so-called fingerprint database drawing refers to drawing a “signal field strength map” (fingerprint database) by extracting signal features (Bluetooth RSSI) in an area in which indoor positioning needs to be performed. At the stage of real-time positioning, a user compares a received signal with signals in the “signal field strength map” and the position of the user based on universal algorithms such as a particle filtering algorithm to match a user's position. When the fingerprint database is drawn, the longer the signal acquisition time is and the more the sampling points are, the more accurate the fingerprint database is, the more the positioning accuracy is improved; however, the time costs and other expenses are higher at the same time.
At the stage of real-time positioning, most positioning methods based on the particle filtering algorithm adopt acceleration sensors, gyroscopes and the like in a mobile terminal to measure step number and moving directions, and adopt empirical values of step length to compute position changes of the mobile terminals. Consequently, methods for positioning by adopting empirical values of step length easily cause great position update errors due to the fact that users are different and the specific environments are different.